ModelPriceWatch.com
Last scan 2026-10-12 Models tracked 281 Providers 37 Cheapest paid Granite 4.0 H Micro $0.017/Mtok in Every price links to its source

GPT-5 nano

by OpenAI

Current budget cheap tier
Email me when GPT-5 nano gets cheaper
Availability: OpenAI still prices the bare gpt-5-nano alias per token on its own rate card and announces no shutdown date for it. The pinned snapshot gpt-5-nano-2025-08-07 is separately scheduled to shut down on December 11, 2026; that date applies to the pinned snapshot, not to the gpt-5-nano alias, so this row carries no shutdown date. If OpenAI later names the bare alias itself, this row ages to Deprecated on that date. Checked against OpenAI's deprecations page on 2026-08-25.

Today's price · per 1M tokens

Input

$0.050

per 1M tokens

Output

$0.400

per 1M tokens

Blended

$0.138

blended $/1M — 3:1 weighted input:output

Cached input

$0.005

10% of input — prompt caching

Source: official OpenAI pricing · read MODELPRICEWATCH.COM · 2026-10-12

Price receipt

We read OpenAI's own pricing page on and found gpt-5-nano listed with a price on the same row — that read is where the number above comes from, recorded as a new model. We keep the page text we read; its content hash is 0359f17f7e.

Overview

The fastest and most cost-efficient member of the GPT-5 line. 400K context window, 272K max input tokens, 128K max output tokens, text + image input, text output, May 2024 knowledge cutoff. The default snapshot behind the alias is gpt-5-nano-2025-08-07.

Capabilities

struck through = not supported
Input 2/4
Text ✓ Image ✓ Audio Video
Output
Text ✓
Features 3/9
Prompt caching ✓ Reasoning Coding Fast inference Long context ✓ Open weights Multimodal ✓ Web search Realtime

Benchmark performance

accuracy % · higher is better
Percentile vs all tracked models 32.5th1 independent measurement
Percentile per $/Mtok 232.1

We hold no per-benchmark accuracy scores for this model yet, so it has no accuracy average. That is a gap in our coverage, not a sign the model is untested — independent evaluators often publish a composite index for a new model long before releasing its per-benchmark numbers. The percentile above is its standing across the independent composites below.

Independent composite scores
  • AA-Omniscience Index: -28.7 (−100–100)
How it stacks up
  • Ranks #119 of 194 comparably-measured models by percentile score, across 1 independent measurement
  • Ranks #9 of 194 comparably-tested models by normalized performance per dollar

Source: Artificial Analysis · updated · See full rankings →

Specifications

Provider
OpenAI
Context window
400K tokens
Modality
text, image
Parameters
Proprietary
Open source
No — proprietary
Released
Status
Current
Last updated
Tags
budgetmultimodal

Availability verified: — listed on OpenAI's own page

Try on OpenAI Official pricing page